PhD Oral Exam - Morteza Zangenehsoroush, Information and Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Design creativity—the ability to generate novel, useful, and unexpected ideas—is a complex, nonlinear, and recursive cognitive process, as shown by theoretical and experimental studies. Unlike well-defined problem-solving, it involves iterative, open-ended exploration, making its neurocognitive mechanisms difficult to capture. While many models describe creativity as nonlinear, recursive, and co-evolutionary, neuroscientific empirical validation remains limited.

This thesis addresses this gap by applying advanced electroencephalography (EEG) analyses to investigate brain dynamics across four cognitive states: idea generation, idea evolution, idea rating, and rest. The brain, as a complex system, exhibits nonlinear and recursive dynamics across interacting lobes that support design and creativity cognition, dynamics often overlooked by traditional deterministic approaches.

A multi-phase methodology was introduced. First, nonlinear EEG features (entropy, fractal dimensions, Lyapunov and correlation dimensions) were employed to assess nonlinear theoretical models. Second, recurrence quantification analysis (RQA) quantified EEG recursive patterns and validated the recursive nature of design and creativity cognition. Third, functional connectivity based on weighted phase lag index (wPLI) and mutual information (MI) revealed inter-channel and inter-lobe interactions unique to each state.

Statistical analyses, feature selection, and classification were performed in each phase. Topographic mapping localized significant EEG markers, and machine learning models confirmed their discriminative power, yielding high classification accuracy.

Results confirm the nonlinear and recursive nature of design creativity and reveal complex neural interactions underlying distinct cognitive states. This thesis provides one of the first comprehensive EEG-based validations of theoretical models of design creativity, offering a robust framework for future research.